Abstract:

The minor component analysis neural network is a method for adaptively extract the minor component of the autocorrelation matrix of the input data,which has been researched in the last decade.This paper analyzes and summarizes the minor component analysis learning algorithm as general divergence,sudden divergence,dynamic divergence,numerical divergence and selfstabilizing property,and points out the existing problems and the future development trend of this fields,and this work lays sound theoretical foundations for the neural MCA theory.

Key words: neural network；minor component analysis；selfstabilizing property